SALES TREND ANALYSIS WITH MACHINE LEARNING LINEAR REGRESSION ALGORITHM METHOD

ALWIDAH YANI SIPAHUTAR, NPM 2009100078 (2024) SALES TREND ANALYSIS WITH MACHINE LEARNING LINEAR REGRESSION ALGORITHM METHOD. Tugas_Akhir (Artikel) Sinkron : Jurnal dan Penelitian Teknik Informatika, 8 (3). pp. 1724-1728. ISSN 2541-2019 (e-ISSN)/ 2541-044X (p-ISSN)

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Abstract

The development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process. Keywords : Analysis, Trend, Linear Regression, Rapidminer

Item Type: Article
Uncontrolled Keywords: Analysis, Trend, Linear Regression, Rapidminer
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User: Unnamed user with email repository@ulb.ac.id
Date Deposited: 12 Sep 2024 03:00
Last Modified: 12 Sep 2024 03:00
URI: http://repository.ulb.ac.id/id/eprint/1067

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